The present invention relates to elasticity imaging including but not limited to strain imaging and in particular to an improved method of determining displacement vectors used to produce such images.
Strain imaging produces images revealing the underlying elastic parameters of the material being measured. When used in medicine, strain imaging is analogous to palpation by a physician, that is, the pressing of tissue by the physician to feel differences in elasticity in the underlying structures.
In a common form of strain imaging, two separate images are obtained with the measured material in different states of deformation, typically, as deformed by an external force. In ultrasound strain imaging, the ultrasound probe itself may be used to provide this deformation.
The two images are analyzed to deduce the amount of displacement in the material at a number of corresponding regions. The gradient in these displacements, determined as a function of the spatial location of the regions, provides strain information generally reflecting the elasticity of the tissue. An example of such strain imaging and a description of techniques for determining displacement of tissue between two images are described in detail in U.S. Pat. No. 6,508,768 and in pending application Ser. No. 12/258,532 filed Oct. 27, 2008 and entitled: Ultrasonic Strain Imaging Device with Selectable Cost-Function, both hereby incorporated by reference.
The displacement between corresponding regions of the material in the first and second state of deformation can be determined by identifying a multi-point region (i.e. a reference kernel) in the material in the first state of deformation and moving this kernel within a two- or three-dimensional search region over a search region of the material in the second state of deformation. The displacement vector is determined by the best match between the reference kernel and its overlapping portion onto the search region of the material in the second state of deformation (i.e. the target kernel). The best match may be determined by evaluating a similarity of the data of the reference and target kernels for example, as a sum of the magnitude of differences between individual samples of these two kernels or other similar technique.
The matching process as used to determine the relative displacement of the material during deformation is subject to an error termed “peak hopping” in which the reference kernel falsely matches to a target kernel in the second image that represents “non-physical” motion, for example, as a result of random noise dominating the similarity determination. Such peak hopping becomes more acute with small kernels (necessary for high resolution imaging) or when there is substantial tissue displacement.
Peak hopping can be reduced by limiting the search region of the kernel based on a priori assumptions about the movement of the tissue, for example, that the trend of displacement in tissue will be continuous over adjacent regions reflecting the continuous nature of the tissue itself. In one implementation of this technique, the location of the search region of a given kernel is guided by the displacement vector of a previously evaluated adjacent kernel.
This assumption of continuity in tissue breaks down for many types of tissue where the tissue is inhomogeneous or where there are sliding interfaces, for example, between organs. Further, use of this of technique, where the evaluation of previous reference/target kernels guides the evaluation of later kernels, can result in “downstream tracking errors” when errors in the evaluation of earlier kernels are propagated downstream to the later kernels and kernels evaluated from those later kernels.
One method for reducing downstream tracking errors was proposed by Chen et al as discussed in “A Quality-Guided Displacement Tracking Algorithm For Ultrasonic Elasticity Imaging” in Medical Image Analysis 13 (2009) 286-296. In the Chen approach, displacement vectors for a computed kernel are used to guide the search region for later kernels only if there are no other displacement vectors that result in higher “quality” between the reference and target kernels. Correlation between the kernels was used by Chen et al. as the measure of quality. In this way, low-quality kernels carry less priority and could be discarded, given the presence of large number of high correlation kernels, thereby reducing the downstream tracking errors. One additional advantage of the Chen approach is that the computation of displacement vectors propagates through kernels in the direction that is flexibly directed by the resultant correlation of the displacement vectors estimated between reference and target kernels. Consequently, if the direction of propagation of the calculation confronts regions with low signal correlation (that might introduce errors) the direction of propagation of the calculations shifts to flow around the low signal correlation regions rather than through them. Because the propagation of the calculation follows the correlation of the data, tissue interfaces tend to be arrived at by calculations propagating from opposite sides of the interface and terminating at the interface rather than passing through the interface such as would introduce downstream tracking errors.
The starting point for the calculation of displacement vectors used in Chen relies upon one or a few “seed” displacement vectors and their associated reference/target kernels spread within the images that are qualified to have high similarity. It is important that these seed displacement vectors and kernels have high correlation between the reference kernel and its best match to the target kernel, because they affect many subsequent calculations and any errors in these seed displacement vectors and kernels will create downstream tracking errors in many other regions. Yet these isolated seed displacement vectors and associated kernels, particularly because of their isolation, are highly susceptible to “peak hopping” errors in which similarity does not reveal an underlying displacement error.